JACIII Vol.27 No.6 pp. 1142-1150
doi: 10.20965/jaciii.2023.p1142

Research Paper:

Task Assignment of UAV Swarms Based on Auction Algorithm in Poor Communication Environments

Zihao Chen ORCID Icon, Juan Li, Chang Liu, and Jie Li

Beijing Institute of Technology
No.5 South Street, Zhongguancun, Haidian District, Beijing 100081, China

Corresponding author

April 4, 2023
July 19, 2023
November 20, 2023
task assignment, poor communication environments, information fusion, information integrity, information authenticity

Poor communication environments always lead to unstable communication in unmanned aerial vehicle swarms. To solve the problem of task assignment in poor communication environments, this study proposed an information fusion strategy (IFS) based on information integrity and authenticity. The proposed IFS was embedded into the classical sequential and the Prim assignment and its generalization (G-Prim) decentralized task assignment algorithms, and these two improved variants with the proposed IFS were denoted as sequential auction with IFS (Seq-IFS) and G-Prim-IFS, respectively. The Bernoulli and Gilbert–Elliott models, which can model communication delay and packet loss, were adopted to describe unstable communication channels. A series of test instances with different swarm sizes and levels of communication channel reliability was used to test the performances of Seq-IFS and G-Prim-IFS in their original forms. Numerical experimental results demonstrated that the proposed Seq-IFS and G-Prim-IFS significantly outperformed their original versions in most test instances, particularly in cases with low communication environments.

Cite this article as:
Z. Chen, J. Li, C. Liu, and J. Li, “Task Assignment of UAV Swarms Based on Auction Algorithm in Poor Communication Environments,” J. Adv. Comput. Intell. Intell. Inform., Vol.27 No.6, pp. 1142-1150, 2023.
Data files:
  1. [1] S. Waharte and N. Trigoni, “Supporting search and rescue operations with UAVs,” 2010 Int. Conf. on Emerging Security Technologies, pp. 142-147, 2010.
  2. [2] T. R. Gulden et al., “Modeling rapidly composable, heterogeneous, and fractionated forces: Findings on mosaic warfare from an agent-based model,” RAND Corp., 2021.
  3. [3] M. Otte, M. J. Kuhlman, and D. Sofge, “Auctions for multi-robot task allocation in communication limited environments,” Autonomous Robots, Vol.44, No.3, pp. 547-584, 2020.
  4. [4] L. Sultan et al., “Communication among heterogeneous unmanned aerial vehicles (UAVs): Classification, trends, and analysis,” IEEE Access, Vol.9, pp. 118815-118836, 2021.
  5. [5] R. W. Beard and T. W. McLain, “Multiple UAV cooperative search under collision avoidance and limited range communication constraints,” 42nd IEEE Int. Conf. on Decision and Control, Vol.1, pp. 25-30, 2003.
  6. [6] M. Hoeing et al., “Auction-based multi-robot task allocation in COMSTAR,” Proc. of the 6th Int. Joint Conf. on Autonomous Agents and Multiagent Systems (AAMAS’07), Article No.280, 2007.
  7. [7] D. P. Bertsekas, “A distributed algorithm for the assignment problem,” Laboratory for Information and Decision Systems Working Paper, Massachusetts Institute of Technology, 1979.
  8. [8] M. J. Matarić and G. S. Sukhatme, “Task-allocation and coordination of multiple robots for planetary exploration,” Proc. of the 10th Int. Conf. on Advanced Robotics (ICAR), pp. 61-70, 2001.
  9. [9] M. G. Lagoudakis et al., “Simple auctions with performance guarantees for multi-robot task allocation,” 2004 IEEE/RSJ Int. Conf. on Intelligent Robots and Systems (IROS), Vol.1, pp. 698-705, 2004.
  10. [10] P. Stone and M. Veloso, “Task decomposition, dynamic role assignment, and low-bandwidth communication for real-time strategic teamwork,” Artificial Intelligence, Vol.110, No.2, pp. 241-273, 1999.
  11. [11] P. B. Sujit and J. B. Sousa, “Multi-UAV task allocation with communication faults,” 2012 American Control Conf. (ACC), pp. 3724-3729, 2012.
  12. [12] M. Otte, M. Kuhlman, and D. Sofge, “Competitive target search with multi-agent teams: Symmetric and asymmetric communication constraints,” Autonomous Robots, Vol.42, No.6, pp. 1207-1230, 2018.
  13. [13] Z. Chen et al., “Decentralized task assignment based on information fusion in communication–constrained environments,” The 10th Int. Symp. on Computational Intelligence and Industrial Applications (ISCIIA), Session No.A5-2, 2022.
  14. [14] D. Dionne and C. A. Rabbath, “Multi-UAV decentralized task allocation with intermittent communications: The DTC algorithm,” 2007 American Control Conf. (ACC), pp. 5406-5411, 2007.
  15. [15] H.-L. Choi, L. Brumet, and J. P. How, “Consensus-based decentralized auctions for robust task allocation,” IEEE Trans. on Robotics, Vol.25, No.4, pp. 912-926, 2009.
  16. [16] A. A. Khuwaja et al., “A survey of channel modeling for UAV communications,” IEEE Communications Surveys & Tutorials, Vol.20, No.4, pp. 2804-2821, 2018.
  17. [17] S. Kumar and S. Rai, “Survey on transport layer protocols: TCP & UDP,” Int. J. of Computer Applications, Vol.46, Nol.7, pp. 20-25, 2012. https://doi.oeg/10.5120/6920-9285
  18. [18] F. T. Al-Dhief et al., “Performance comparison between TCP and UDP protocols in different simulation scenarios,” Int. J. of Engineering & Technology, Vol.7, No.4.36, pp. 172-176, 2018.
  19. [19] E. F. Nakamura, A. A. F. Loureiro, and A. C. Frery, “Information fusion for wireless sensor networks: Methods, models, and classifications,” ACM Computing Surveys, Vol.39, No.3, 2007.

*This site is desgined based on HTML5 and CSS3 for modern browsers, e.g. Chrome, Firefox, Safari, Edge, Opera.

Last updated on Jul. 12, 2024